Abstract
Text Categorization (TC)-the assignment of predefined categories to documents of a corpus-plays an important role in a wide variety of information organization and management tasks of Information Retrieval (IR). It involves the management of a lot of information, but some of them could be noisy or irrelevant and hence, a previous feature reduction could improve the performance of the classification. In this paper we proposed a wrapper approach. This kind of approach is timeconsuming and sometimes could be infeasible. But our wrapper explores a reduced number of feature subsets and also it uses Support Vector Machines (SVM) as the evaluation system; and this two properties make the wrapper fast enough to deal with large number of features present in text domains. Taking the Reuters-21578 corpus, we also compare this wrapper with the common approach for feature reduction widely applied in TC, which consists of filtering according to scoring measures.
The research reported in this paper has been supported in part under MCyT and Feder grant TIC2001-3579 and FICYT grant BP01-114.
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Montañés, E., Quevedo, J., Díaz, I. (2003). A Wrapper Approach with Support Vector Machines for Text Categorization. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_30
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DOI: https://doi.org/10.1007/3-540-44868-3_30
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